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• W2889673996 abstract "Uncertainty quantification of a technical system can be done using density estimation. We usually start with a stochastic model, which is fitted to the technical system, and the density estimation is done using data from this stochastic model. However, in any application, such a stochastic model will not be perfect, and the estimation of the density should take into account the inadequacy of the stochastic model. In this paper, we show how observed data of the real system together with an imperfect simulation model can be used to derive confidence bands for the density of the technical system. Our main result is that the newly introduced confidence bands allow to derive lower and upper bounds on the probability of intervals in the technical system. Furthermore, we present an upper bound on the area of the confidence band in case of a smooth density. The results are illustrated by applying the estimates to simulated and real data." @default.
• W2889673996 created "2018-09-27" @default.
• W2889673996 creator A5028420890 @default.
• W2889673996 creator A5046164638 @default.
• W2889673996 date "2019-03-01" @default.
• W2889673996 modified "2023-10-03" @default.
• W2889673996 title "Estimation of a Density From an Imperfect Simulation Model" @default.
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• W2889673996 doi "https://doi.org/10.1109/tit.2018.2871048" @default.
• W2889673996 hasPublicationYear "2019" @default.
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